Code used to generate paper results : Database comparison and CT characteristics
Julie Devis
2023-08-28
1 Loading data and packages
library(readxl)
library(readr)
library(CTexploreR)
library(Vennerable)
library(biomaRt)
library(tidyverse)
library(SummarizedExperiment)
library(UpSetR)
library(ComplexHeatmap)
library(circlize)
library(SingleCellExperiment)
library(org.Hs.eg.db)
library(clusterProfiler)
library(msigdbr)
library(DOSE)
library(BiocParallel)
library(patchwork)
library(Biostrings)Gene names/synonyms required for databases cleaning
ensembl <- biomaRt::useDataset("hsapiens_gene_ensembl", useMart("ensembl"))
attributes_vector <- c("ensembl_gene_id", "external_gene_name",
"external_synonym", "gene_biotype",
"chromosome_name", "band", "start_position", "end_position",
"strand")
ensembl_gene_synonym <- as_tibble(getBM(attributes = attributes_vector, mart = ensembl))
ensembl_gene_synonym <- ensembl_gene_synonym %>%
mutate(external_synonym = sub(pattern = "ORF", external_synonym,
replacement = "orf"))
attributes_vector <- c("ensembl_gene_id", "external_gene_name")
ensembl_gene_names <- as_tibble(getBM(attributes = attributes_vector, mart = ensembl))
attributes_vector <- c("external_gene_name",
"external_synonym")
gene_synonym <- as_tibble(getBM(attributes = attributes_vector, mart = ensembl))# CCLE_data <- CTdata::CCLE_data()
# TCGA_TPM <- CTdata::TCGA_TPM()
# testis_sce <- CTdata::testis_sce()
####
load("../CTdata/eh_data/CCLE_data.rda")
load("../CTdata/eh_data/TCGA_TPM.rda")
load("../CTdata/eh_data/testis_sce.rda")
load("../CTdata/eh_data/mean_methylation_in_tissues.rda")
load("../CTdata/eh_data/all_genes.rda")
load("../CTdata/eh_data/HPA_cell_type_specificities.rda")
load("../CTdata/eh_data/CT_genes.rda")
CT_and_CTP_genes <- CT_genes
CT_genes <- (filter(CT_genes, CT_gene_type == "CT_gene"))Common figures parameters
legends_param <- list(
labels_gp = gpar(col = "black", fontsize = 6),
title_gp = gpar(col = "black", fontsize = 6),
row_names_gp = gpar(fontsize = 4),
annotation_name_side = "left")
legend_colors <- c("#5E4FA2", "#3288BD", "#66C2A5", "#ABDDA4", "#E6F598",
"#FFFFBF", "#FEE08B", "#FDAE61", "#F46D43", "#D53E4F",
"#9E0142")
chr_colors <- c("X-linked" = "deeppink", "Not X" = "royalblue1")
meth_colors <- c("Methylation" = "lightgreen", "Not methylation" = "gray")2 Pipeline figure values
Here is the code used to have all the values in the pipeline figure
table(all_genes$GTEX_category)##
## lowly_expressed other testis_preferential testis_specific
## 2658 19741 535 1568table(all_genes$multimapping_analysis)##
## not_testis_specific testis_preferential testis_specific
## 2575 7 76table(all_genes$testis_specificity)##
## not_testis_specific testis_preferential testis_specific
## 22316 607 1579specific_to_pref <- all_genes %>%
filter(GTEX_category == "testis_specific" |
multimapping_analysis == "testis_specific") %>%
filter(testis_specificity == "testis_preferential")
table(specific_to_pref$not_detected_in_somatic_HPA)##
## FALSE
## 65table(specific_to_pref$CCLE_category)##
## activated leaky not_activated
## 26 1 38table(specific_to_pref$TCGA_category)##
## activated leaky multimapping_issue not_activated
## 28 10 2 25all_genes %>%
filter(testis_specificity != "not_testis_specific") %>%
filter(CCLE_category == "activated" &
(TCGA_category == "multimapping_issue" |
TCGA_category == "activated")) %>%
pull(testis_specificity) %>%
table()## .
## testis_preferential testis_specific
## 70 229table(all_genes$CT_gene_type) #Before and after TSS verif##
## CT_gene CTP_gene other
## 176 62 242643 Database comparison
3.1 Lists cleaning
CT lists from other databases have been checked (using GTEx and our
GTEx_expression() funtion and GeneCards) in order to remove
duplicated gene names or deprecated ones and allow comparison between
databases.
3.1.1 CTdatabase
Online list copied in a csv file, several lists exist so we combined them.
We checked gene names that were a concatenation of two genes (choice using biomaRt synonyms to get the official one), checked which ones had the right names, removed duplicated genes, verified lost genes and added back those that should be there.
CTdatabase <- read_delim("data/CTdatabase1.csv", delim = ";",
escape_double = FALSE, trim_ws = TRUE)
colnames(CTdatabase) <- c("Family", "Gene_Name", "Chromosomal_localization",
"CT_identifier")
CTdatabase_bis <- read_csv2("data/CTdatabase2.csv")
CTdatabase <- left_join(CTdatabase, CTdatabase_bis,
by = c("Gene_Name" = "Gene_Symbol"))
CTdatabase_single <- CTdatabase %>%
mutate(Gene_Name = sub(pattern = "/.*$", Gene_Name, replacement = ""))
CTdatabase_single <- CTdatabase_single %>%
mutate(Gene_Name = sub(pattern = ",.*$", Gene_Name, replacement = ""))
CTdatabase_official_names <-
unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id,
external_gene_name)) %>%
filter(external_gene_name %in% CTdatabase_single$Gene_Name) %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA)
CTdatabase_synonym <-
ensembl_gene_synonym %>%
filter(external_synonym %in% CTdatabase_single$Gene_Name) %>%
mutate(Gene_Name = external_synonym) %>%
dplyr::select(ensembl_gene_id, external_gene_name, Gene_Name, external_synonym)
CTdatabase_cleaned <-
rbind(CTdatabase_official_names, CTdatabase_synonym) %>%
left_join(CTdatabase_single)
duplicated_genes <- CTdatabase_cleaned$Gene_Name[duplicated(CTdatabase_cleaned$Gene_Name)]
bad_ids <- ensembl_gene_synonym %>%
filter(external_gene_name %in% duplicated_genes | external_synonym %in% duplicated_genes) %>%
filter(chromosome_name %in% grep(pattern = "H", x = chromosome_name, value = TRUE)) %>%
pull(ensembl_gene_id)
CTdatabase_cleaned <- CTdatabase_cleaned %>%
dplyr::filter(!ensembl_gene_id %in% bad_ids)
CTdatabase_cleaned <- CTdatabase_cleaned %>%
filter(!ensembl_gene_id == "ENSG00000052126")
CTdatabase_cleaned <- CTdatabase_cleaned %>%
filter(!(ensembl_gene_id == "ENSG00000183305" & Gene_Name == "MAGEA2"))
CTdatabase_cleaned <- CTdatabase_cleaned %>%
filter(!ensembl_gene_id == "ENSG00000204648")
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "CSAG3B")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "CSAG2", "external_synonym"] <- "CSAG3B"
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "CT45A4")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "CT45A3", "external_synonym"] <- "CT45A4"
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "LAGE-1b")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "CTAG2", "external_synonym"] <- "LAGE-1b"
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "CT16.2")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "PAGE5", "external_synonym"] <- "CT16.2"
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "SPANXB2")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "SPANXB1", "external_synonym"] <- "SPANXB2"
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "SPANXE")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "SPANXD", "external_synonym"] <- "SPANXE"
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "XAGE1C")
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "XAGE1D")
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "XAGE1E")
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "XAGE2B")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "XAGE2", "external_synonym"] <- "XAGE2B"
CTdatabase_cleaned <- filter(CTdatabase_cleaned, Gene_Name != "CTAGE-2")
CTdatabase_cleaned[CTdatabase_cleaned$Gene_Name == "CTAGE1", "external_synonym"] <- "CTAGE-2"
CTdatabase_cleaned <- ensembl_gene_synonym %>%
mutate(Gene_Name = external_synonym) %>%
filter(external_synonym == "CXorf61") %>%
dplyr::select(ensembl_gene_id, external_gene_name, Gene_Name, external_synonym) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "Cxorf61",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name)) %>%
filter(external_gene_name == "CCNA1") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "cyclin A1",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name))%>%
filter(external_gene_name == "GOLGA6L2") %>%
filter(ensembl_gene_id == "ENSG00000174450") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "GOLGAGL2 FA",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name))%>%
filter(external_gene_name == "LYPD6B") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "LOC130576",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name))%>%
filter(external_gene_name == "CT62") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "LOC196993",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name))%>%
filter(external_gene_name == "CT75") %>%
filter(ensembl_gene_id == "ENSG00000291155") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "LOC440934",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name))%>%
filter(external_gene_name == "LINC01192") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "LOC647107",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name))%>%
filter(external_gene_name == "TSPY1") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "LOC728137",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)
CTdatabase_cleaned <- unique(dplyr::select(ensembl_gene_synonym, ensembl_gene_id, external_gene_name))%>%
filter(external_gene_name == "SSX2B") %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA) %>%
cbind(CTdatabase_single[CTdatabase_single$Gene_Name == "SSX2b",
c("Family", "Chromosomal_localization", "CT_identifier", "Classification")]) %>%
rbind(CTdatabase_cleaned)3.1.2 Jamin’s list
Excel file coming from supplemental data.
Jamin_core_CT <- read_excel("data/Jamin_core_CT.xlsx")
Jamin_core_CT[Jamin_core_CT$Gene == "KIAA1211", "Gene"] <- "CRACD"
Jamin_core_CT[Jamin_core_CT$Gene == "CXorf67", "Gene"] <- "EZHIP"3.1.3 Wang’s CTatlas
Excel file coming from supplemental data.
Wang_CT <- read_excel("data/Wang_Suppl_Data_3.xlsx",
sheet = "Supplementary Data 3B", skip = 1)
colnames(Wang_CT)[1] <- "ensembl_gene_id"
Wang_CT <- ensembl_gene_names %>%
filter(ensembl_gene_id %in% Wang_CT$ensembl_gene_id) %>%
right_join(Wang_CT)
Wang_CT[Wang_CT$ensembl_gene_id == "ENSG00000181013", "external_gene_name"] <- "C17orf47"
Wang_CT[Wang_CT$ensembl_gene_id == "ENSG00000204293", "external_gene_name"] <- "OR8B2"
Wang_CT[Wang_CT$external_gene_name == "", "external_gene_name"] <- "RNASE11"
Wang_CT[Wang_CT$external_gene_name == "CHCT1", "external_gene_name"] <- "C17orf64"
Wang_CT[Wang_CT$external_gene_name == "PRSS40A", "external_gene_name"] <- "TISP43"
Wang_CT[Wang_CT$external_gene_name == "TEX56P", "external_gene_name"] <- "C6orf201"
Wang_CT[Wang_CT$external_gene_name == "SLC25A51P4", "external_gene_name"] <- "RP11-113D6.10"
Wang_CT[Wang_CT$external_gene_name == "TCP10L3", "external_gene_name"] <- "TCP10"
Wang_CT[Wang_CT$external_gene_name == "SCAND3", "external_gene_name"] <- "ZBED9"3.1.4 Carter’s list
Carter_CT_list <- read_table("data/Carter_CT_list.txt", skip = 1)
Carter_CT <- Carter_CT_list[Carter_CT_list$CT_Expression,]
Carter_CT[Carter_CT$Gene == "ENSG00000261649", "Gene_Name"] <- "GOLGA6L7"
Carter_CT[Carter_CT$Gene == "ENSG00000239620", "Gene_Name"] <- "PRR20G"
Carter_CT[Carter_CT$Gene == "ENSG00000168148", "Gene_Name"] <- "H3-4"
Carter_CT[Carter_CT$Gene == "ENSG00000204296", "Gene_Name"] <- "TSBP1"
Carter_CT[Carter_CT$Gene == "ENSG00000180219", "Gene_Name"] <- "GARIN6"
Carter_CT[Carter_CT$Gene == "ENSG00000172717", "Gene_Name"] <- "GARIN2"
Carter_CT[Carter_CT$Gene == "ENSG00000174015", "Gene_Name"] <- "CBY2"
Carter_CT[Carter_CT$Gene == "ENSG00000224960", "Gene_Name"] <- "PPP4R3C"3.1.5 Burggeman’s list
Excel file from supplemental data.
Bruggeman_data <- read_excel("data/Bruggeman_suppl_data.xlsx", skip = 1,
sheet = "1D")
Bruggeman_official_names <- gene_synonym %>%
dplyr::select(external_gene_name) %>%
unique() %>%
filter(external_gene_name %in% Bruggeman_data$Gene) %>%
mutate(Gene_Name = external_gene_name) %>%
mutate(external_synonym = NA)
Bruggeman_synonym <- gene_synonym %>%
filter(external_synonym %in% Bruggeman_data$Gene) %>%
mutate(Gene_Name = external_synonym) %>%
dplyr::select(external_gene_name, Gene_Name, external_synonym)
Bruggeman_synonym <- Bruggeman_synonym[-which(Bruggeman_synonym$Gene_Name %in%
Bruggeman_official_names$Gene_Name),]
Bruggeman_CT <- rbind(Bruggeman_official_names, Bruggeman_synonym)
lost <- Bruggeman_data[which(!Bruggeman_data$Gene %in% c(Bruggeman_CT$Gene_Name)), "Gene"]
colnames(lost) <- "external_gene_name"
lost$Gene_Name <- rep(NA, nrow(lost))
lost$external_synonym <- rep(NA, nrow(lost))
lost[lost$external_gene_name == "C21orf59", "Gene_Name"] <- "CFAP298"
lost[lost$external_gene_name == "C11orf57", "Gene_Name"] <- "NKAPD1"
lost[lost$external_gene_name == "C7orf55", "Gene_Name"] <- "FMC1"
lost[lost$external_gene_name == "C10orf12", "Gene_Name"] <- "LCOR"
lost[lost$external_gene_name == "RPL19P12", "Gene_Name"] <- "RPL19P12"
lost[lost$external_gene_name == "C16orf59", "Gene_Name"] <- "TEDC2"
lost[lost$external_gene_name == "TTTY15", "Gene_Name"] <- "USP9Y"
lost[lost$external_gene_name == "C17orf53", "Gene_Name"] <- "HROB"
lost[lost$external_gene_name == "C1orf112", "Gene_Name"] <- "FIRRM"
lost[lost$external_gene_name == "C12orf66", "Gene_Name"] <- "KICS2"
lost[lost$external_gene_name == "C9orf84", "Gene_Name"] <- "SHOC1"
lost[lost$external_gene_name == "C10orf25", "Gene_Name"] <- "ZNF22-AS1"
lost[lost$external_gene_name == "C20orf197", "Gene_Name"] <- "LINC02910"
lost[lost$external_gene_name == "C3orf67", "Gene_Name"] <- "CFAP20DC"
lost[lost$external_gene_name == "C8orf37", "Gene_Name"] <- "CFAP418"
lost[lost$external_gene_name == "C22orf34", "Gene_Name"] <- "MIR3667HG"
Bruggeman_CT <- rbind(Bruggeman_CT, lost)
missing_Bruggeman <- c("BMS1P4", "ADAM6", "ANXA2P3", "ARHGAP11B", "DPY19L2P2",
"HLA-L", "PA2G4P4", "PIPSL", "PRKY", "YBX3P1",
"RPL23AP53", "UQCRBP1", "RPL23P8", "MRS2P2", "PIN4P1",
"SLC6A10P", "GUSBP2", "PPIEL", "LRRC37BP1", "MSL3P1",
"PLEKHA8P1", "STAG3L1", "TCAM1P", "ZNF702P", "ZNF815P",
"ATP6AP1L", "RPL21P44", "SEC14L1P1", "ZNF876P",
"RPLP0P2", "FAM86JP", "FAM175A", "LACE1", "ATP5EP2",
"WDR92", "TCTE3", "METTL20", "KIAA2022", "ZNRD1-AS1",
"SGOL1", "FAM35DP", "MTL5", "TMEM14E", "MLLT4-AS1",
"CCDC173", "KIAA1524", "WDR78", "LINC00476", "LYRM5",
"HILS1", "CASC5", "KIAA1919", "CTAGE5", "FAM188B",
"TMEM194B", "FAM122C", "PPP1R2P3", "KIAA0391", "SGOL2",
"FAM19A3", "ZNF788", "RPL19P12", "FIRRM")
external_names_to_keep <- gene_synonym %>%
filter(external_synonym %in% missing_Bruggeman) %>%
filter(!external_gene_name %in% c("ATP5F1EP2", "POLR1HASP", "SHLD2P3",
"TMEM14EP", "H1-9P", "ZNF788P")) %>%
mutate(Gene_Name = external_gene_name)
Bruggeman_CT[Bruggeman_CT$external_synonym %in%
external_names_to_keep$external_synonym,
"Gene_Name"] <- external_names_to_keep$Gene_Name
Bruggeman_CT <- Bruggeman_CT %>%
dplyr::select(Gene_Name)3.2 CTexploreR data for selection pipeline
To characterise the differences between our database and other, we
need the category we created in CTexploreR. For this, we have the object
all_genes in CTdata that contains the CT analysis for all
genes. More info in
Hereunder is what we used for our selection pipeline (coming from
make_all_genes_prelim.R and
130_make_all_genes_and_CT_genes.R in
CTdata).
all_genesFrom there, we filtered based on the testis_specificity (“testis_specific”, which is based on expression in health tissue and scRNA seq info from HPA), CCLE_category (“activated”) and TCGA_category (“activated” or “multimapping_issue”) to have our CT genes. Then, when wanting to validate TSS manually, we realised that for some genes, reads were not properly aligned to exons which might reflect a poorly defined transcription in these regions and are hence likely unreliable.
Some genes were also characterized as Cancer-Testis preferential genes when testis specificity was less stringent
3.3 CTexploreR VS CTdatabase
CTdatabase_ours <- Venn(list(CTdatabase = CTdatabase_cleaned$external_gene_name,
CTexploreR = CT_genes$external_gene_name))
gp <- VennThemes(compute.Venn(CTdatabase_ours))
gp[["Face"]][["11"]]$fill <- "mistyrose"
gp[["Face"]][["01"]]$fill <- "darkseagreen1"
gp[["Face"]][["10"]]$fill <- "lightsteelblue1"
gp[["Set"]][["Set1"]]$col <- "paleturquoise4"
gp[["Set"]][["Set2"]]$col <- "darkseagreen4"
gp[["SetText"]][["Set1"]]$col <- "paleturquoise4"
gp[["SetText"]][["Set2"]]$col <- "darkseagreen4"
plot(CTdatabase_ours, gp = gp)
We find 29.0322581 % of CTdatabase in CTexploreR, which is 40.9090909% of our database.
Lost genes analysis
CTdatabase_lost <- all_genes %>%
filter(external_gene_name %in% CTdatabase_ours@IntersectionSets[["10"]])
# 9 Genes are lost because not in any database
table(CTdatabase_lost$testis_specificity)##
## not_testis_specific testis_preferential testis_specific
## 70 54 41table(CTdatabase_lost$CT_gene_type)##
## CTP_gene other
## 15 150table(CTdatabase_lost$not_detected_in_somatic_HPA)##
## FALSE TRUE
## 51 106table(CTdatabase_lost$TCGA_category)##
## activated leaky multimapping_issue not_activated
## 34 83 25 23table(CTdatabase_lost$CCLE_category)##
## activated leaky not_activated
## 81 33 51table(CTdatabase_lost$TCGA_category, CTdatabase_lost$CCLE_category)##
## activated leaky not_activated
## activated 20 0 14
## leaky 41 33 9
## multimapping_issue 15 0 10
## not_activated 5 0 18CTdatabase_lost_upset <-
list(`Not testis specific` =
filter(CTdatabase_lost,
testis_specificity != "testis_specific")$external_gene_name,
`Not tumour activated` =
filter(CTdatabase_lost,
(TCGA_category != "activated" &
TCGA_category != "multimapping_issue")|
CCLE_category != "activated")$external_gene_name,
`CT preferential` =
filter(CTdatabase_lost,
CT_gene_type == "CTP_gene")$external_gene_name)
upset_CTdatabase <- fromList(CTdatabase_lost_upset)
upset(upset_CTdatabase)
(nrow(CTdatabase_lost) - table(CTdatabase_lost$testis_specificity)[["testis_specific"]])/nrow(CTdatabase_lost)*100## [1] 75.1515275.1515152 % of these genes are not testis specific.
However 15 of these lost genes are flagged as Cancer-Testis preferential in our analysis.
87.8787879 % are not properly activated in tumors and/or cancer cell lines.
In their analysis, they had characterised gene specificity, some being not available, not found in testis, testis-restricted, testis-selective and testis/brain-restricted. Let’s see how the lost genes qualify as they didn’t mention those were strictly testis specific.
CTdatabase_cleaned %>%
filter(external_gene_name %in% CTdatabase_lost$external_gene_name) %>%
pull(Classification) %>%
table()## .
## not available not found in testis testis-restricted
## 90 2 15
## testis-selective testis/brain-restricted
## 51 7We can see that most of them had no info or were testis-selective (I couldn’t find on website or paper how they selected categories).
3.4 CTexploreR VS omics databases
core_ours <- Venn(list(Jamin = Jamin_core_CT$Gene,
CTexploreR = CT_genes$external_gene_name))
Wang_ours <- Venn(list(Wang = Wang_CT$external_gene_name,
CTexploreR = CT_genes$external_gene_name))
Carter_ours <- Venn(list(Carter_CT = Carter_CT$Gene_Name,
CTexploreR = CT_genes$external_gene_name))
Bruggeman_ours <- Venn(list(Bruggeman = Bruggeman_CT$Gene_Name,
CTexploreR = CT_genes$external_gene_name))
gene_list <- list(CTexploreR = CT_genes$external_gene_name,
Carter = Carter_CT$Gene_Name,
Jamin = Jamin_core_CT$Gene,
CTatlas = Wang_CT$external_gene_name,
Bruggeman = Bruggeman_CT$Gene_Name)
upset_omics <- fromList(gene_list)
upset(upset_omics)
4 in all, 60 in at least 3 databases
Lost genes analysis
plot(core_ours, gp = gp)
Jamin_lost <- all_genes %>%
filter(external_gene_name %in% core_ours@IntersectionSets[["10"]])
table(Jamin_lost$testis_specificity)##
## not_testis_specific testis_preferential testis_specific
## 70 20 2table(Jamin_lost$CT_gene_type)##
## CTP_gene other
## 10 82table(Jamin_lost$not_detected_in_somatic_HPA)##
## FALSE TRUE
## 44 48table(Jamin_lost$TCGA_category)##
## activated leaky multimapping_issue not_activated
## 8 63 17 4table(Jamin_lost$CCLE_category)##
## activated leaky not_activated
## 49 39 4table(Jamin_lost$TCGA_category, Jamin_lost$CCLE_category)##
## activated leaky not_activated
## activated 8 0 0
## leaky 22 39 2
## multimapping_issue 17 0 0
## not_activated 2 0 2Jamin_lost_upset <-
list(`Not testis specific` =
filter(Jamin_lost,
testis_specificity != "testis_specific")$external_gene_name,
`Not tumour activated` =
filter(Jamin_lost,
(TCGA_category != "activated" &
TCGA_category != "multimapping_issue")|
CCLE_category != "activated")$external_gene_name,
`CT preferential` =
filter(Jamin_lost,
CT_gene_type == "CTP_gene")$external_gene_name)
upset_Jamin <- fromList(Jamin_lost_upset)
upset(upset_Jamin)
We find 23.2 % of CTdatabase in CTexploreR, which is 16.4772727 % of our database.
97.826087% of these genes are not testis specific.
However 10 of these lost genes are flagged as Cancer-Testis preferential in our analysis.
91.3043478 % are not properly activated in tumors and/or cancer cell lines.
plot(Wang_ours, gp = gp)
Wang_lost <- all_genes %>%
filter(external_gene_name %in% Wang_ours@IntersectionSets[["10"]])
table(Wang_lost$testis_specificity)##
## not_testis_specific testis_preferential testis_specific
## 336 348 229table(Wang_lost$CT_gene_type)##
## CTP_gene other
## 40 873table(Wang_lost$not_detected_in_somatic_HPA)##
## FALSE TRUE
## 254 628table(Wang_lost$TCGA_category)##
## activated leaky multimapping_issue not_activated
## 191 474 66 182table(Wang_lost$CCLE_category)##
## activated leaky not_activated
## 342 206 365table(Wang_lost$TCGA_category, Wang_lost$CCLE_category)##
## activated leaky not_activated
## activated 88 3 100
## leaky 188 201 85
## multimapping_issue 34 0 32
## not_activated 32 2 148Wang_lost_upset <-
list(`Not testis specific` =
filter(Wang_lost,
testis_specificity != "testis_specific")$external_gene_name,
`Not tumour activated` =
filter(Wang_lost,
(TCGA_category != "activated" &
TCGA_category != "multimapping_issue")|
CCLE_category != "activated")$external_gene_name,
`CT preferential` =
filter(Wang_lost,
CT_gene_type == "CTP_gene")$external_gene_name)
upset_Wang <- fromList(Wang_lost_upset)
upset(upset_Wang)
We find 9.0284593 % of CTdatabase in CTexploreR, which is 52.2727273 % of our database.
74.9178532% of these genes are not testis specific.
However 40 of these lost genes are flagged as Cancer-Testis preferential in our analysis.
90.3614458 % are not properly activated in tumors and/or cancer cell lines.
plot(Carter_ours, gp = gp)
Carter_lost <- all_genes %>%
filter(external_gene_name %in% Carter_ours@IntersectionSets[["10"]])
table(Carter_lost$testis_specificity)##
## not_testis_specific testis_preferential testis_specific
## 1 22 34table(Carter_lost$CT_gene_type)##
## CTP_gene other
## 6 51table(Carter_lost$not_detected_in_somatic_HPA)##
## FALSE TRUE
## 13 44table(Carter_lost$TCGA_category)##
## activated leaky not_activated
## 28 14 15table(Carter_lost$CCLE_category)##
## activated leaky not_activated
## 19 3 35table(Carter_lost$TCGA_category, Carter_lost$CCLE_category)##
## activated leaky not_activated
## activated 9 0 19
## leaky 6 3 5
## not_activated 4 0 11Carter_lost_upset <-
list(`Not testis specific` =
filter(Carter_lost,
testis_specificity != "testis_specific")$external_gene_name,
`Not tumour activated` =
filter(Carter_lost,
(TCGA_category != "activated" &
TCGA_category != "multimapping_issue")|
CCLE_category != "activated")$external_gene_name,
`CT preferential` =
filter(Carter_lost,
CT_gene_type == "CTP_gene")$external_gene_name)
upset_Carter <- fromList(Carter_lost_upset)
upset(upset_Carter)
We find 38.8349515 % of CTdatabase in CTexploreR, which is 22.7272727 % of our database.
40.3508772% of these genes are not testis specific.
However 6 of these lost genes are flagged as Cancer-Testis preferential in our analysis.
84.2105263 % are not properly activated in tumors and/or cancer cell lines.
plot(Bruggeman_ours, gp = gp)
Bruggeman_lost <- all_genes %>%
filter(external_gene_name %in% Bruggeman_ours@IntersectionSets[["10"]])
table(Bruggeman_lost$testis_specificity)##
## not_testis_specific testis_preferential testis_specific
## 627 61 11table(Bruggeman_lost$CT_gene_type)##
## CTP_gene other
## 6 693table(Bruggeman_lost$not_detected_in_somatic_HPA)##
## FALSE TRUE
## 441 234table(Bruggeman_lost$TCGA_category)##
## activated leaky multimapping_issue not_activated
## 46 624 11 18table(Bruggeman_lost$CCLE_category)##
## activated leaky not_activated
## 242 428 29table(Bruggeman_lost$TCGA_category, Bruggeman_lost$CCLE_category)##
## activated leaky not_activated
## activated 32 3 11
## leaky 193 418 13
## multimapping_issue 9 0 2
## not_activated 8 7 3Bruggeman_lost_upset <-
list(`Not testis specific` =
filter(Bruggeman_lost,
testis_specificity != "testis_specific")$external_gene_name,
`Not tumour activated` =
filter(Bruggeman_lost,
(TCGA_category != "activated" &
TCGA_category != "multimapping_issue")|
CCLE_category != "activated")$external_gene_name,
`CT preferential` =
filter(Bruggeman_lost,
CT_gene_type == "CTP_gene")$external_gene_name)
upset_Bruggeman <- fromList(Bruggeman_lost_upset)
upset(upset_Bruggeman)
We find 1.7195767 % of CTdatabase in CTexploreR, which is 7.3863636 % of our database.
98.4263233% of these genes are not testis specific.
However 6 of these lost genes are flagged as Cancer-Testis preferential in our analysis.
95.4220315 % are not properly activated in tumors and/or cancer cell lines.
3.5 Characterisation of differences with all databases
common <- unique(c(core_ours@IntersectionSets[["11"]],
CTdatabase_ours@IntersectionSets[["11"]],
Wang_ours@IntersectionSets[["11"]],
Carter_ours@IntersectionSets[["11"]],
Bruggeman_ours@IntersectionSets[["11"]]))
length(common)## [1] 119length(common)/dim(CT_genes)[1] * 100## [1] 67.61364lost_list <- unique(c(core_ours@IntersectionSets[["10"]],
CTdatabase_ours@IntersectionSets[["10"]],
Wang_ours@IntersectionSets[["10"]],
Carter_ours@IntersectionSets[["10"]],
Bruggeman_ours@IntersectionSets[["10"]]))
lost <- all_genes %>%
filter(external_gene_name %in% lost_list)
not_specific <- filter(lost, testis_specificity == "not_testis_specific")
GTEX_expression(not_specific$external_gene_name, units = "log_TPM")## Error in readRDS(file) : error reading from connection## Warning: 5 out of 989 names invalid: FIRRM, CFAP96, SPMIP5, LIAT1, BLTP2.
## See the manual page for valid types.
somatic_testis <- filter(lost, not_detected_in_somatic_HPA == FALSE)
testis_expression(somatic_testis$external_gene_name, cells = "all")## Warning: 11 out of 710 names invalid: MPL, EDAR, ASB14, F2RL2, PCDHGB2, FOXO3B,
## KRT33B, SEMG1, ADORA2A, DAZ2, DAZ4.
## See the manual page for valid types.## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 columns You can control `use_raster` argument by explicitly
## setting TRUE/FALSE to it.
##
## Set `ht_opt$message = FALSE` to turn off this message.
HPA_cell_type_expression(somatic_testis$external_gene_name)
not_TCGA_activated <- filter(lost, TCGA_category != "activated" &
TCGA_category != "multimapping_issue")
TCGA_expression(not_TCGA_activated$external_gene_name,
tumor = "all",
units = "log_TPM")## Warning: 16 out of 1283 names invalid: FIRRM, CIMIP2C, CFAP96, SPMIP10, SPMIP4,
## SPMIP7, SPATA31F1, SPMIP5, CIMAP1A, SAXO4, CIMAP1C, LIAT1, BLTP2,
## SPMAP2, CIMAP1D, SAXO5.
## See the manual page for valid types.## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 columns You can control `use_raster` argument by explicitly
## setting TRUE/FALSE to it.
##
## Set `ht_opt$message = FALSE` to turn off this message.
not_CCLE_activated <- filter(lost, CCLE_category != "activated")
CCLE_expression(not_CCLE_activated$external_gene_name,
type = c("lung", "skin", "colorectal",
"gastric", "breast", "head_and_neck"),
units = "log_TPM")## Warning: 11 out of 1034 names invalid: FIRRM, SPMIP10, SPMIP4, SPATA31F1,
## CIMIP2A, CIMAP1A, SAXO4, CIMAP1C, LIAT1, BLTP2, CIMAP1D.
## See the manual page for valid types.
transcript_prob <- lost %>%
filter(testis_specificity == "testis_specific" |
testis_specificity == "testis_preferential") %>%
filter(TCGA_category == "activated" | TCGA_category == "multimapping_issue") %>%
filter(CCLE_category == "activated") %>%
dim()119 genes in our CTexploreR database are found in at least one of the other database, which represents 67.6136364%.
We have lost 1693 genes in total. Among them, 58.4170112% are not considered testis specific, 41.9373892% are expressed in somatic cells, 75.7826344% are not activated in TCGA samples, 61.0750148% are not activated in CCLE cell lines and 3.4258712% is lost due to transcripts problems.
What about new genes in CTexploreR
new <- CT_genes %>%
filter(!external_gene_name%in%common)
newtable(new$testis_specificity)##
## testis_specific
## 57table(new$X_linked)##
## FALSE TRUE
## 46 11table(new$regulated_by_methylation)##
## FALSE TRUE
## 23 34table(new$X_linked, new$regulated_by_methylation)##
## FALSE TRUE
## FALSE 23 23
## TRUE 0 11TCGA_expression(tumor = "all", genes = new$external_gene_name,
units = "log_TPM")## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 columns You can control `use_raster` argument by explicitly
## setting TRUE/FALSE to it.
##
## Set `ht_opt$message = FALSE` to turn off this message.
TCGA_expression(tumor = "all",
genes = filter(new, X_linked & regulated_by_methylation)$external_gene_name,
units = "log_TPM")## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 columns You can control `use_raster` argument by explicitly
## setting TRUE/FALSE to it.
##
## Set `ht_opt$message = FALSE` to turn off this message.
There are 57 new CT genes in CTexploreR. These are all testis specific and mainly on autosomes. Regulation by methylation is the majority of them. There is only 11 new “major” CT that are on the X chromosome and regulated by methylation. CT45 are not that new.
Expression in tumours doesn’t strike that much.
4 CT genes characteristics
table(CT_genes$testis_specificity)##
## testis_specific
## 176table(CT_genes$transcript_biotype)##
## lncRNA protein_coding
## 34 142Most genes are testis specific (100%). Most genes are mainly protein coding genes (80.6818182%).
4.1 X chromosome and regulated by methylation
In CTexploreR, genes have been characterised as regulated by methylation or not.
table(CT_genes$X_linked)##
## FALSE TRUE
## 98 78table(CT_genes$regulated_by_methylation)##
## FALSE TRUE
## 47 129table(CT_genes$X_linked, CT_genes$testis_specificity)##
## testis_specific
## FALSE 98
## TRUE 78table(CT_genes$X_linked, CT_genes$regulated_by_methylation)##
## FALSE TRUE
## FALSE 43 55
## TRUE 4 74Genes are enriched on the X chromosome (44.3181818%). Also, 129 genes have been flagged as regulated by methylation (73.2954545%). It is interesting to study the link between these two characteristics.
On the chromosome X, there is a clear enrichment of CT genes regulated by methylation (74/78 chrX genes or 74/129 genes regulated by methylation).
Let’s check that with a statistical test, I here want to see if, on there is an enrichment of genes regulated by methylation on the X chromosome. I need to do a Pearson Chi square test (to know if observed proportion differ from expected proportion). It is a statistical method to determine if two categorical variables have a significant correlation between them. I can directly put a matrix (my table) in the function
chisq.test(table(CT_genes$X_linked, CT_genes$regulated_by_methylation))##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: table(CT_genes$X_linked, CT_genes$regulated_by_methylation)
## X-squared = 31.367, df = 1, p-value = 2.135e-08CT_genes$chr_factor <- factor(CT_genes$chr,
levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9",
"10", "11", "12", "13", "14", "15", "16", "17",
"18", "19", "20", "21", "22", "X", "Y"))
CT_genes %>%
mutate(regulated_by_methylation = ifelse(regulated_by_methylation,
"Regulated by methylation",
"Not regulated by methylation")) %>%
ggplot(aes(x = chr_factor, fill = X_linked)) +
geom_bar(stat = 'count') +
scale_fill_manual(values = c("royalblue1", "deeppink")) +
facet_wrap(~ regulated_by_methylation, ncol = 2) +
theme_bw() +
xlab("Chromosome") +
ylab("Number of genes") +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "none",
axis.title = element_text(size = 10, color = "gray10"))
There is indeed a significative link between regulation by methylation and being on the X chromosome. There is thus an enrichment of CT genes regulated by methylation on the X chromosome (and inversely).
4.2 Tumour and methylation
Using CTexploreR functions, we can explore all CT genes or focus on some potential targets.
4.2.1 All CT
For these heatmaps, the code comes from the function but has been copies to add some annotations.
chr_mat <- as.matrix(CT_genes$X_linked)
chr_mat <- ifelse(chr_mat == TRUE, "X-linked", "Not X")
rownames(chr_mat) <- CT_genes$external_gene_name
row_ha_chr <- rowAnnotation(chr_factor = chr_mat,
annotation_legend_param = legends_param,
simple_anno_size = unit(0.5, "cm"),
col = list(chr_factor = chr_colors),
annotation_name_gp = gpar(fontsize = 8),
annotation_name_side = "top")
regulation_mat <- as.matrix(CT_genes$regulated_by_methylation)
regulation_mat <- ifelse(regulation_mat == TRUE, "Methylation",
"Not methylation")
rownames(regulation_mat) <- CT_genes$external_gene_name
row_ha_reg <- rowAnnotation(regulation = regulation_mat,
annotation_legend_param = legends_param,
simple_anno_size = unit(0.5, "cm"),
col = list(regulation = meth_colors),
annotation_name_gp = gpar(fontsize = 8),
annotation_name_side = "top")
left_annot <- c(row_ha_chr, row_ha_reg, gap = unit(1, "mm"))
split <- data.frame(CT_genes$regulated_by_methylation, CT_genes$X_linked)database <- TCGA_TPM
database$tumor <- sub(pattern = 'TCGA-', x = database$project_id, '')
database$type <- "Tumor"
database$type[database$shortLetterCode == "NT"] <- "Peritumoral"
database <- database[, order(database$tumor, database$type)]
genes <- CT_genes$external_gene_name
database <- database[rowData(database)$external_gene_name %in% genes, ]
database <- database[match(genes, rowData(database)$external_gene_name), ]
database <- database[, database$type == "Tumor"]
column_ha_tumor <- HeatmapAnnotation(
Tumor = database$tumor,
border = TRUE,
col = list(Tumor = c("BRCA" = "midnightblue", "COAD" = "darkorchid2",
"ESCA" = "gold", "HNSC" = "deeppink2",
"LUAD" = "seagreen", "LUSC" = "seagreen3",
"SKCM" = "red3")),
annotation_name_gp = gpar(fontsize = 8),
annotation_legend_param = legends_param)
split_by <- factor(database$tumor)
col_annot <- column_ha_tumor
mat <- log1p(assay(database))
rownames(mat) <- rowData(database)$external_gene_name
Heatmap(mat[, , drop = FALSE],
name = "logTPM",
column_title = paste0("Expression in TCGA samples (all)"),
column_split = split_by,
row_split = split,
row_title_gp = gpar(fontsize = 0),
col = colorRamp2(seq(0, max(mat), length = 11),
legend_colors),
clustering_method_rows = "ward.D",
clustering_method_columns = "ward.D",
cluster_rows = TRUE,
show_column_names = FALSE,
cluster_columns = TRUE,
show_column_dend = FALSE,
show_row_dend = FALSE,
row_names_gp = gpar(fontsize = 4),
heatmap_legend_param = legends_param,
top_annotation = col_annot,
left_annotation = left_annot)
database <- CCLE_data
database$type <- tolower(database$type)
genes <- CT_genes$external_gene_name
database <- database[rowData(database)$external_gene_name %in% genes, ]
database <- database[match(genes, rowData(database)$external_gene_name), ]
mat <- log1p(assay(database))
rownames(mat) <- rowData(database)$external_gene_name
df_col <- data.frame("cell_line" = colData(database)$cell_line_name,
"type" = as.factor(colData(database)$type))
rownames(df_col) <- rownames(colData(database))
df_col <- df_col[order(df_col$type), ]
column_ha_type <- HeatmapAnnotation(
type = df_col$type,
border = TRUE,
annotation_name_gp = gpar(fontsize = 8),
annotation_legend_param = legends_param,
col = list(type = c("lung" = "seagreen3", "skin" = "red3",
"bile_duct" = "mediumpurple1", "bladder" = "mistyrose2",
"colorectal" = "plum", "lymphoma" = "steelblue1",
"uterine" = "darkorange4", "myeloma" = "turquoise3",
"kidney" = "thistle4",
"pancreatic" = "darkmagenta", "brain" = "palegreen2",
"gastric" = "wheat3", "breast" = "midnightblue",
"bone" = "sienna1", "head_and_neck" = "deeppink2",
"ovarian" = "tan3", "sarcoma" = "lightcoral",
"leukemia" = "steelblue4", "esophageal"= "khaki",
"neuroblastoma" = "olivedrab1")))
Heatmap(mat[, rownames(df_col), drop = FALSE],
name = "logTPM",
column_title = "Gene Expression in tumor cell lines (CCLE)",
column_split = factor(df_col$type),
row_split = split,
row_title_gp = gpar(fontsize = 0),
col = colorRamp2(seq(0, max(mat), length = 11),
legend_colors),
clustering_method_rows = "ward.D",
clustering_method_columns = "ward.D",
cluster_rows = TRUE,
show_row_dend = FALSE,
show_column_names = FALSE,
cluster_columns = TRUE,
show_column_dend = FALSE,
row_names_gp = gpar(fontsize = 4),
heatmap_legend_param = legends_param,
top_annotation = c(column_ha_type),
left_annotation = left_annot)
Next graph was removed from paper
genes <- CT_genes$external_gene_name
database <- mean_methylation_in_tissues[rownames(mean_methylation_in_tissues) %in% genes]
mat <- na.omit(assay(database))
clustering_option <- TRUE
row_ha_chr_meth <- rowAnnotation(chr = chr_mat[rownames(mat),],
annotation_legend_param = legends_param,
simple_anno_size = unit(0.5, "cm"),
col = list(chr = chr_colors),
annotation_name_gp = gpar(fontsize = 8),
annotation_name_side = "top")
row_ha_reg_meth <- rowAnnotation(regulation = regulation_mat[rownames(mat),],
annotation_legend_param = legends_param,
simple_anno_size = unit(0.5, "cm"),
col = list(regulation = meth_colors),
annotation_name_gp = gpar(fontsize = 8),
annotation_name_side = "top")
left_annot_meth <- c(row_ha_chr_meth, row_ha_reg_meth, gap = unit(1, "mm"))
split_meth <- data.frame(filter(CT_genes, external_gene_name %in% rownames(mat))$regulated_by_methylation,
filter(CT_genes, external_gene_name %in% rownames(mat))$X_linked)
Heatmap(mat,
column_title = 'Promoter mean methylation level by tissue',
name = 'Meth',
col = colorRamp2(c(1:100),
colorRampPalette(c("moccasin","dodgerblue4"))(100)),
na_col = "gray80",
cluster_rows = clustering_option,
cluster_columns = FALSE,
row_split = split_meth,
row_title_gp = gpar(fontsize = 0),
show_row_names = TRUE,
show_heatmap_legend = TRUE,
show_row_dend = FALSE,
row_names_gp = gpar(fontsize = 3),
column_names_gp = gpar(fontsize = 8),
column_names_side = "bottom",
row_names_side = "right",
left_annotation = left_annot_meth)
4.2.2 MAGE genes
MAGE_genes <- filter(CT_genes, family == "MAGE")$external_gene_name
TCGA_expression(tumor = "all",
genes = MAGE_genes,
units = "log_TPM")## `use_raster` is automatically set to TRUE for a matrix with more than
## 2000 columns You can control `use_raster` argument by explicitly
## setting TRUE/FALSE to it.
##
## Set `ht_opt$message = FALSE` to turn off this message.
CCLE_expression(genes = MAGE_genes,
type = c("lung", "skin", "bile_duct", "bladder",
"colorectal", "lymphoma", "uterine",
"myeloma", "kidney", "pancreatic", "brain",
"gastric", "breast", "bone", "head_and_neck",
"ovarian", "sarcoma", "leukemia", "esophageal",
"neuroblastoma"), units = "log_TPM")
normal_tissues_mean_methylation(MAGE_genes, na.omit = TRUE)
4.3 Single Cell analysis : timing of expression
In CTexploreR, we there is single cell data from the testis, we thus can analyse CT genes expression during spermatogenesis.
There is only data for 137 of our 176 CT genes.
NB : SSC, Spermatogonia and early spermatocytes are premeiotic cells. Late spermatocytes (between both meiosis), round spermatid, elongated spermatid and sperm are postmeiotic cells.
genes_avail <-
CT_genes$external_gene_name[CT_genes$external_gene_name %in% unique(rownames(testis_sce))]
table(CT_genes$testis_cell_type)## Warning: Unknown or uninitialised column: `testis_cell_type`.## < table of extent 0 >table(CT_genes$testis_cell_type)/length(genes_avail)*100## Warning: Unknown or uninitialised column: `testis_cell_type`.## numeric(0)NA % of genes are mainly expressed pre-meioticly.
germ_cells <- c("SSC", "Spermatogonia", "Early_spermatocyte",
"Late_spermatocyte","Round_spermatid", "Elongated_spermatid",
"Sperm1", "Sperm2")
somatic_cells <- c("Macrophage", "Endothelial", "Myoid", "Sertoli", "Leydig")
testis_sce_CT <- testis_sce[genes_avail, ]
mat <- SingleCellExperiment::logcounts(testis_sce_CT)
df_col <- data.frame(clusters = colData(testis_sce_CT)$clusters,
type = colData(testis_sce_CT)$type,
Donor = colData(testis_sce_CT)$Donor)
rownames(df_col) <- colnames(testis_sce_CT)
df_col <- df_col[order(df_col$type),]
df_col$lineage <- "Germ cells"
df_col$lineage[df_col$type %in% somatic_cells] <- "Somatic cells"
column_ha_type = HeatmapAnnotation(
type = df_col$type,
border = TRUE,
col = list(type = c("SSC" = "floralwhite", "Spermatogonia" = "moccasin",
"Early_spermatocyte" = "gold",
"Late_spermatocyte" = "orange",
"Round_spermatid" = "red2",
"Elongated_spermatid" = "darkred",
"Sperm1" = "violet", "Sperm2" = "purple",
"Sertoli" = "gray",
"Leydig" = "cadetblue2", "Myoid" = "springgreen3",
"Macrophage" = "gray10",
"Endothelial" = "steelblue")),
annotation_name_gp = gpar(fontsize = 8),
annotation_legend_param = legends_param)
column_ha_lineage = HeatmapAnnotation(
lineage = df_col$lineage,
border = TRUE,
col = list(lineage = c("Germ cells" = "salmon", "Somatic cells" = "cyan4")),
annotation_name_gp = gpar(fontsize = 8),
annotation_legend_param = legends_param)
scale_lims <- c(0, quantile(rowMax(mat), 0.75))
top_annot <- c(column_ha_lineage, column_ha_type)
# Until here is what's in the function, hereunder is my addition/change in Heatmap()
CT_genes_avail <- filter(CT_genes, external_gene_name %in% genes_avail)
chr_mat <- as.matrix(CT_genes_avail$X_linked)
chr_mat <- ifelse(chr_mat == TRUE, "X-linked", "Not X")
rownames(chr_mat) <- CT_genes_avail$external_gene_name
row_ha_chr <- rowAnnotation(chr = chr_mat,
annotation_legend_param = legends_param,
simple_anno_size = unit(0.5, "cm"),
col = list(chr = chr_colors),
annotation_name_gp = gpar(fontsize = 8),
annotation_name_side = "top")
regulation_mat <- as.matrix(CT_genes_avail$regulated_by_methylation)
regulation_mat <- ifelse(regulation_mat == TRUE, "Methylation",
"Not methylation")
rownames(regulation_mat) <- CT_genes_avail$external_gene_name
row_ha_reg <- rowAnnotation(regulation = regulation_mat,
annotation_legend_param = legends_param,
simple_anno_size = unit(0.5, "cm"),
col = list(regulation = meth_colors),
annotation_name_gp = gpar(fontsize = 8),
annotation_name_side = "top")
left_annot <- c(row_ha_chr, row_ha_reg, gap = unit(1, "mm"))
split <- data.frame(CT_genes_avail$regulated_by_methylation, CT_genes_avail$X_linked)
Heatmap(mat[genes_avail, rownames(df_col), drop = FALSE],
name = "logCounts",
column_title = "Expression in testis cells (scRNAseq)",
column_split = df_col$type,
row_split = split,
row_title_gp = gpar(fontsize = 0),
show_column_names = FALSE,
show_column_dend = FALSE,
clustering_method_rows = "ward.D",
clustering_method_columns = "ward.D",
cluster_rows = TRUE,
cluster_columns = FALSE,
show_row_dend = FALSE,
row_names_gp = gpar(fontsize = 4),
col = colorRamp2(seq(scale_lims[1], scale_lims[2], length = 11),
legend_colors),
top_annotation = top_annot,
left_annotation = left_annot,
heatmap_legend_param = legends_param)
4.4 Gene function
All CT genes function
msigdbr(species = "Homo sapiens" , category = "C5") %>%
filter(gene_symbol %in% CT_genes$external_gene_name) %>%
pull(gene_symbol) %>%
unique() %>%
length()## [1] 106msigdbr(species = "Homo sapiens" , category = "H") %>%
filter(gene_symbol %in% CT_genes$external_gene_name) %>%
pull(gene_symbol) %>%
unique() %>%
length()## [1] 7go_ora <- enrichGO(gene = CT_genes$ensembl_gene_id,
keyType = "ENSEMBL",
OrgDb = org.Hs.eg.db,
ont = "all",
readable = TRUE)
as_tibble(go_ora)as_tibble(go_ora) %>%
arrange(desc(Count)) %>%
head(12) %>%
mutate(Ratio = case_when(ONTOLOGY == "BP"~ Count/177,
ONTOLOGY == "CC"~ Count/197,
ONTOLOGY == "MF"~ Count/186)) %>%
ggplot(aes(x = Ratio, y = Description, fill = Description)) +
geom_col() +
theme_bw() +
ylab("GO term") +
xlab("Gene Ratio") +
theme(axis.text.y = element_blank(),
legend.position = "none",
axis.ticks.y = element_blank(),
axis.title = element_text(size = 10, color = "gray10"))+
geom_text(aes(0, y = Description, label = Description),
hjust = 0,
nudge_x = 0.005,
colour = "floralwhite",
size = 4)
ora_to_plot <- as_tibble(simplify(go_ora))
ora_to_plot <- ora_to_plot %>%
arrange(desc(Count)) %>%
head(9) %>%
mutate(Ratio = case_when(ONTOLOGY == "BP"~ Count/102,
ONTOLOGY == "MF"~ Count/186))
ora_to_plot %>%
ggplot(aes(x = Ratio, y = Description, fill = Description)) +
geom_col() +
theme_bw() +
ylab("GO term") +
xlab("Gene Ratio") +
theme(axis.text.y = element_blank(),
legend.position = "none",
axis.ticks.y = element_blank(),
axis.title = element_text(size = 10, color = "gray10"))+
geom_text(aes(0, y = Description, label = Description),
hjust = 0,
nudge_x = 0.005,
colour = "floralwhite",
size = 3.7)
As we can see here, most of genes are indeed linked to functions from reproduction. I represented here the 12 categories with the most genes, all from biological processes. However, they are enriched in 54 different GO terms.
Is there a difference between meth reg or not ?
go_ora_meth <- enrichGO(gene =
filter(CT_genes, regulated_by_methylation)$ensembl_gene_id,
keyType = "ENSEMBL",
OrgDb = org.Hs.eg.db,
ont = "all",
readable = TRUE)
go_ora_meth <- simplify(go_ora_meth)
as_tibble(go_ora_meth)go_ora_not_meth <- enrichGO(gene =
filter(CT_genes, !regulated_by_methylation)$ensembl_gene_id,
keyType = "ENSEMBL",
OrgDb = org.Hs.eg.db,
ont = "all",
readable = TRUE)
go_ora_not_meth <- simplify(go_ora_not_meth)
as_tibble(go_ora_not_meth)go_ora_meth_plot <- as_tibble(go_ora_meth) %>%
arrange(desc(Count)) %>%
head(10) %>%
mutate(Ratio = case_when(ONTOLOGY == "BP"~ Count/73,
ONTOLOGY == "MF"~ Count/80,
ONTOLOGY == "CC"~ Count/78)) %>%
mutate(regulation = "Methylation")
go_ora_not_meth_plot <- as_tibble(go_ora_not_meth) %>%
arrange(desc(Count)) %>%
head(8) %>%
mutate(Ratio = case_when(ONTOLOGY == "BP"~ Count/29,
ONTOLOGY == "MF"~ Count/30))%>%
mutate(regulation = "Not methylation")
rbind(go_ora_meth_plot, go_ora_not_meth_plot) %>%
ggplot(aes(x = Ratio, y = Description, fill = Description)) +
geom_col() +
theme_bw() +
ylab("GO term") +
xlab("Gene Ratio") +
facet_wrap(~ regulation) +
theme(legend.position = "none",
axis.ticks.y = element_blank(),
axis.text.y = element_text(size = 6, color = "gray10"),
axis.title.x = element_text(size = 10, color = "gray10"),
axis.title.y = element_blank())+
geom_text(aes(0, y = Description, label = ID),
hjust = 0,
nudge_x = 0.005,
colour = "floralwhite",
size = 2)
We’ve also added a column tumor suppressor and oncogene in CTexploreR. These information come from Cancermine.
table(CT_genes$oncogene) ##
## oncogene
## 23table(CT_genes$tumor_suppressor)##
## tumor_suppressor
## 6table(CT_genes$oncogene, CT_genes$tumor_suppressor) ##
## tumor_suppressor
## oncogene 45 SessionInfo
sessionInfo()## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Brussels
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] Biostrings_2.70.3 XVector_0.42.0
## [3] patchwork_1.2.0 BiocParallel_1.36.0
## [5] DOSE_3.28.2 msigdbr_7.5.1
## [7] clusterProfiler_4.10.1 org.Hs.eg.db_3.18.0
## [9] AnnotationDbi_1.64.1 SingleCellExperiment_1.24.0
## [11] circlize_0.4.16 ComplexHeatmap_2.18.0
## [13] UpSetR_1.4.0 SummarizedExperiment_1.32.0
## [15] Biobase_2.62.0 GenomicRanges_1.54.1
## [17] GenomeInfoDb_1.38.7 IRanges_2.36.0
## [19] S4Vectors_0.40.2 MatrixGenerics_1.14.0
## [21] matrixStats_1.2.0 lubridate_1.9.3
## [23] forcats_1.0.0 stringr_1.5.1
## [25] dplyr_1.1.4 purrr_1.0.2
## [27] tidyr_1.3.1 tibble_3.2.1
## [29] ggplot2_3.5.0 tidyverse_2.0.0
## [31] biomaRt_2.58.2 Vennerable_3.0
## [33] xtable_1.8-4 gtools_3.9.5
## [35] reshape_0.8.9 RColorBrewer_1.1-3
## [37] lattice_0.22-5 RBGL_1.78.0
## [39] graph_1.80.0 BiocGenerics_0.48.1
## [41] CTexploreR_0.99.5 CTdata_1.2.0
## [43] readr_2.1.5 readxl_1.4.3
##
## loaded via a namespace (and not attached):
## [1] splines_4.3.2 later_1.3.2
## [3] ggplotify_0.1.2 bitops_1.0-7
## [5] filelock_1.0.3 cellranger_1.1.0
## [7] polyclip_1.10-6 XML_3.99-0.16.1
## [9] lifecycle_1.0.4 doParallel_1.0.17
## [11] vroom_1.6.5 MASS_7.3-60.0.1
## [13] magrittr_2.0.3 sass_0.4.9
## [15] rmarkdown_2.26 jquerylib_0.1.4
## [17] yaml_2.3.8 httpuv_1.6.14
## [19] cowplot_1.1.3 DBI_1.2.2
## [21] abind_1.4-5 zlibbioc_1.48.2
## [23] ggraph_2.2.1 RCurl_1.98-1.14
## [25] yulab.utils_0.1.4 tweenr_2.0.3
## [27] rappdirs_0.3.3 GenomeInfoDbData_1.2.11
## [29] enrichplot_1.22.0 ggrepel_0.9.5
## [31] tidytree_0.4.6 codetools_0.2-19
## [33] DelayedArray_0.28.0 xml2_1.3.6
## [35] ggforce_0.4.2 tidyselect_1.2.1
## [37] shape_1.4.6.1 aplot_0.2.2
## [39] farver_2.1.1 viridis_0.6.5
## [41] BiocFileCache_2.10.1 jsonlite_1.8.8
## [43] GetoptLong_1.0.5 ellipsis_0.3.2
## [45] tidygraph_1.3.1 iterators_1.0.14
## [47] foreach_1.5.2 tools_4.3.2
## [49] progress_1.2.3 treeio_1.26.0
## [51] Rcpp_1.0.12 glue_1.7.0
## [53] gridExtra_2.3 SparseArray_1.2.4
## [55] xfun_0.42 qvalue_2.34.0
## [57] withr_3.0.0 BiocManager_1.30.22
## [59] fastmap_1.1.1 fansi_1.0.6
## [61] digest_0.6.35 gridGraphics_0.5-1
## [63] timechange_0.3.0 R6_2.5.1
## [65] mime_0.12 colorspace_2.1-0
## [67] Cairo_1.6-2 GO.db_3.18.0
## [69] RSQLite_2.3.5 utf8_1.2.4
## [71] generics_0.1.3 data.table_1.15.2
## [73] prettyunits_1.2.0 graphlayouts_1.1.1
## [75] httr_1.4.7 S4Arrays_1.2.1
## [77] scatterpie_0.2.1 pkgconfig_2.0.3
## [79] gtable_0.3.4 blob_1.2.4
## [81] shadowtext_0.1.3 htmltools_0.5.7
## [83] fgsea_1.28.0 clue_0.3-65
## [85] scales_1.3.0 png_0.1-8
## [87] ggfun_0.1.4 knitr_1.45
## [89] rstudioapi_0.15.0 tzdb_0.4.0
## [91] reshape2_1.4.4 rjson_0.2.21
## [93] nlme_3.1-164 curl_5.2.1
## [95] cachem_1.0.8 GlobalOptions_0.1.2
## [97] BiocVersion_3.18.1 parallel_4.3.2
## [99] HDO.db_0.99.1 pillar_1.9.0
## [101] vctrs_0.6.5 promises_1.2.1
## [103] dbplyr_2.5.0 cluster_2.1.6
## [105] evaluate_0.23 magick_2.8.3
## [107] cli_3.6.2 compiler_4.3.2
## [109] rlang_1.1.3 crayon_1.5.2
## [111] labeling_0.4.3 plyr_1.8.9
## [113] fs_1.6.3 stringi_1.8.3
## [115] viridisLite_0.4.2 babelgene_22.9
## [117] munsell_0.5.0 lazyeval_0.2.2
## [119] GOSemSim_2.28.1 Matrix_1.6-5
## [121] ExperimentHub_2.10.0 hms_1.1.3
## [123] bit64_4.0.5 KEGGREST_1.42.0
## [125] shiny_1.8.0 highr_0.10
## [127] interactiveDisplayBase_1.40.0 AnnotationHub_3.10.0
## [129] igraph_2.0.3 memoise_2.0.1
## [131] bslib_0.6.1 ggtree_3.10.1
## [133] fastmatch_1.1-4 bit_4.0.5
## [135] gson_0.1.0 ape_5.7-1